hivae2 0.5

HIVAE (Handling incomplete heterogeneous data using VAEs. - by Nazabal, et al., DOI: 10.1016/j.patcog.2020.107501, 2020) - Extenstion of implementations as easy to use Python library/tf2 version

hivae2

This repository contains a modular reimplemenation of the "Heterogeneous Incomplete Variational Autoencoder model (HI-VAE) written by Alfredo Nazabal ([e-mail]) et al. .written in Python.

The details of this model can be found are included in this paper. Please cite it if you use this code/library for your own research. This is an extenstion of implementations as easy to use Python library, upgraded for tensorflow2.

Examples

See examples directory for usage

Files description

*(outdated) HIVAE.py: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.

• loglik_ models_ missing_normalize.py: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.
• model_ HIVAE_inputDropout.py: Contains the HI-VAE with input dropout encoder model.
• model_ HIVAE_factorized.py: Contains the HI-VAE with factorized encoder model

Contact

• For questions regarding algorithm --> Alfredo Nazabal: [e-mail]
• For bugs or suggestion regarding this code --> Andreas Karwath: [e-mail]

Comments

This version required tf2. For apple silcone users, please follow : https://developer.apple.com/metal/tensorflow-plugin/

Comments on general_example.py (might be outdated!)

main_directory: where is the project folder

dataset_name: the name of the database (if you want)

types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classes

types:

• count: real values

• cat: categorical 0 or 1

• pos: positive real values

• ordinal: ordinal number

number of dimensions:

• number of possibilities in the categorical variables or 1 in numerical

number of classes:

• number of options (same of number of dimensions for categorical variables)

dataset_path: this is the folder of the csv files

results_path: the output folder for results

network_path: where the models are going to be stored

types_list: the specific type for the dataset you are going to use data_file: the full dataset train_file/ test_file: if the dataset was already splitted

train_data/test_data: pandas dataframes

dim_y: the depth of the network

dim_s/dim_z: dimensions of the embedding